Search (7 results, page 1 of 1)

  • × author_ss:"Ding, Y."
  • × year_i:[2010 TO 2020}
  1. Ding, Y.: Applying weighted PageRank to author citation networks (2011) 0.02 
    0.019216085 = product of:
  0.057648253 = sum of:
    0.036585998 = weight(_text_:web in 4188) [ClassicSimilarity], result of:
      0.036585998 = score(doc=4188,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.25239927 = fieldWeight in 4188, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.0546875 = fieldNorm(doc=4188)
    0.021062255 = product of:
      0.04212451 = sum of:
        0.04212451 = weight(_text_:22 in 4188) [ClassicSimilarity], result of:
          0.04212451 = score(doc=4188,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.2708308 = fieldWeight in 4188, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.0546875 = fieldNorm(doc=4188)
      0.5 = coord(1/2)
  0.33333334 = coord(2/6)
    Abstract
    This article aims to identify whether different weighted PageRank algorithms can be applied to author citation networks to measure the popularity and prestige of a scholar from a citation perspective. Information retrieval (IR) was selected as a test field and data from 1956-2008 were collected from Web of Science. Weighted PageRank with citation and publication as weighted vectors were calculated on author citation networks. The results indicate that both popularity rank and prestige rank were highly correlated with the weighted PageRank. Principal component analysis was conducted to detect relationships among these different measures. For capturing prize winners within the IR field, prestige rank outperformed all the other measures
    Date
    22. 1.2011 13:02:21
  2. Ding, Y.; Jacob, E.K.; Fried, M.; Toma, I.; Yan, E.; Foo, S.; Milojevicacute, S.: Upper tag ontology for integrating social tagging data (2010) 0.01 
    0.009052687 = product of:
  0.054316122 = sum of:
    0.054316122 = weight(_text_:web in 3421) [ClassicSimilarity], result of:
      0.054316122 = score(doc=3421,freq=6.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.37471575 = fieldWeight in 3421, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=3421)
  0.16666667 = coord(1/6)
    Abstract
    Data integration and mediation have become central concerns of information technology over the past few decades. With the advent of the Web and the rapid increases in the amount of data and the number of Web documents and users, researchers have focused on enhancing the interoperability of data through the development of metadata schemes. Other researchers have looked to the wealth of metadata generated by bookmarking sites on the Social Web. While several existing ontologies have capitalized on the semantics of metadata created by tagging activities, the Upper Tag Ontology (UTO) emphasizes the structure of tagging activities to facilitate modeling of tagging data and the integration of data from different bookmarking sites as well as the alignment of tagging ontologies. UTO is described and its utility in modeling, harvesting, integrating, searching, and analyzing data is demonstrated with metadata harvested from three major social tagging systems (Delicious, Flickr, and YouTube).
  3. Sugimoto, C.R.; Li, D.; Russell, T.G.; Finlay, S.C.; Ding, Y.: ¬The shifting sands of disciplinary development : analyzing North American Library and Information Science dissertations using latent Dirichlet allocation (2011) 0.01 
    0.006871327 = product of:
  0.04122796 = sum of:
    0.04122796 = product of:
      0.08245592 = sum of:
        0.08245592 = weight(_text_:programs in 4143) [ClassicSimilarity], result of:
          0.08245592 = score(doc=4143,freq=2.0), product of:
            0.25748047 = queryWeight, product of:
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.044416238 = queryNorm
            0.32024145 = fieldWeight in 4143, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              5.79699 = idf(docFreq=364, maxDocs=44218)
              0.0390625 = fieldNorm(doc=4143)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)
    Abstract
    This work identifies changes in dominant topics in library and information science (LIS) over time, by analyzing the 3,121 doctoral dissertations completed between 1930 and 2009 at North American Library and Information Science programs. The authors utilize latent Dirichlet allocation (LDA) to identify latent topics diachronically and to identify representative dissertations of those topics. The findings indicate that the main topics in LIS have changed substantially from those in the initial period (1930-1969) to the present (2000-2009). However, some themes occurred in multiple periods, representing core areas of the field: library history occurred in the first two periods; citation analysis in the second and third periods; and information-seeking behavior in the fourth and last period. Two topics occurred in three of the five periods: information retrieval and information use. One of the notable changes in the topics was the diminishing use of the word library (and related terms). This has implications for the provision of doctoral education in LIS. This work is compared to other earlier analyses and provides validation for the use of LDA in topic analysis of a discipline.
  4. Huang, Y.; Bu, Y.; Ding, Y.; Lu, W.: From zero to one : a perspective on citing (2019) 0.01 
    0.0065539777 = product of:
  0.039323866 = sum of:
    0.039323866 = weight(_text_:computer in 5387) [ClassicSimilarity], result of:
      0.039323866 = score(doc=5387,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.24226204 = fieldWeight in 5387, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.046875 = fieldNorm(doc=5387)
  0.16666667 = coord(1/6)
    Abstract
    This article investigates the lengths of time that publications with different numbers of citations take to receive their first citation (the beginning stage), and then compares the lengths of time to receive two or more citations after receiving the first citation (the accumulative stage) in the field of computer science. We find that in the beginning stage, that is, from zero to one citation, high-, medium-, and low-cited publications do not obviously exhibit different lengths of time. However, in the accumulative stage, that is, from one to N citations, highly cited publications begin to receive citations much more rapidly than medium- and low-cited publications. Moreover, as N increases, the difference in receiving new citations among high-, medium-, and low-cited publications increases quite significantly.
  5. Bu, Y.; Ding, Y.; Xu, J.; Liang, X.; Gao, G.; Zhao, Y.: Understanding success through the diversity of collaborators and the milestone of career (2018) 0.01 
    0.005461648 = product of:
  0.03276989 = sum of:
    0.03276989 = weight(_text_:computer in 4012) [ClassicSimilarity], result of:
      0.03276989 = score(doc=4012,freq=2.0), product of:
        0.16231956 = queryWeight, product of:
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.044416238 = queryNorm
        0.20188503 = fieldWeight in 4012, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.6545093 = idf(docFreq=3109, maxDocs=44218)
          0.0390625 = fieldNorm(doc=4012)
  0.16666667 = coord(1/6)
    Abstract
    Scientific collaboration is vital to many fields, and it is common to see scholars seek out experienced researchers or experts in a domain with whom they can share knowledge, experience, and resources. To explore the diversity of research collaborations, this article performs a temporal analysis on the scientific careers of researchers in the field of computer science. Specifically, we analyze collaborators using 2 indicators: the research topic diversity, measured by the Author-Conference-Topic model and cosine, and the impact diversity, measured by the normalized standard deviation of h-indices. We find that the collaborators of high-impact researchers tend to study diverse research topics and have diverse h-indices. Moreover, by setting PhD graduation as an important milestone in researchers' careers, we examine several indicators related to scientific collaboration and their effects on a career. The results show that collaborating with authoritative authors plays an important role prior to a researcher's PhD graduation, but working with non-authoritative authors carries more weight after PhD graduation.
  6. Hu, B.; Dong, X.; Zhang, C.; Bowman, T.D.; Ding, Y.; Milojevic, S.; Ni, C.; Yan, E.; Larivière, V.: ¬A lead-lag analysis of the topic evolution patterns for preprints and publications (2015) 0.01 
    0.0052265706 = product of:
  0.031359423 = sum of:
    0.031359423 = weight(_text_:web in 2337) [ClassicSimilarity], result of:
      0.031359423 = score(doc=2337,freq=2.0), product of:
        0.14495286 = queryWeight, product of:
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.044416238 = queryNorm
        0.21634221 = fieldWeight in 2337, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.2635105 = idf(docFreq=4597, maxDocs=44218)
          0.046875 = fieldNorm(doc=2337)
  0.16666667 = coord(1/6)
    Abstract
    This study applied LDA (latent Dirichlet allocation) and regression analysis to conduct a lead-lag analysis to identify different topic evolution patterns between preprints and papers from arXiv and the Web of Science (WoS) in astrophysics over the last 20 years (1992-2011). Fifty topics in arXiv and WoS were generated using an LDA algorithm and then regression models were used to explain 4 types of topic growth patterns. Based on the slopes of the fitted equation curves, the paper redefines the topic trends and popularity. Results show that arXiv and WoS share similar topics in a given domain, but differ in evolution trends. Topics in WoS lose their popularity much earlier and their durations of popularity are shorter than those in arXiv. This work demonstrates that open access preprints have stronger growth tendency as compared to traditional printed publications.
  7. Ding, Y.; Zhang, G.; Chambers, T.; Song, M.; Wang, X.; Zhai, C.: Content-based citation analysis : the next generation of citation analysis (2014) 0.00 
    0.0030088935 = product of:
  0.01805336 = sum of:
    0.01805336 = product of:
      0.03610672 = sum of:
        0.03610672 = weight(_text_:22 in 1521) [ClassicSimilarity], result of:
          0.03610672 = score(doc=1521,freq=2.0), product of:
            0.1555381 = queryWeight, product of:
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.044416238 = queryNorm
            0.23214069 = fieldWeight in 1521, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.046875 = fieldNorm(doc=1521)
      0.5 = coord(1/2)
  0.16666667 = coord(1/6)
    Date
    22. 8.2014 16:52:04

Authors

Themes